Presently, process facilities (e.g., a manufacturing plant, a mineral or crude oil refinery, etc.) are managed using distributed control systems. Contemporary control systems include numerous modules tailored to control or monitor various associated processes of the facility. Conventional means link these modules together to produce the distributed nature of the control system. This affords increased performance and a capability to expand or reduce the control system to satisfy changing facility needs.
Process facility management providers, such as HONEYWELL, INC., develop control systems that can be tailored to satisfy wide ranges of process requirements (e.g., global, local or otherwise) and facility types (e.g., manufacturing, refining, etc.). Such providers have two principle objectives. The first objective is to centralize control of as many processes as possible to improve an overall efficiency of the facility. The second objective is to support a common interface that communicates data among various modules controlling or monitoring the processes, and also with any such centralized controller or operator center.
Each process, or group of associated processes, has certain input (e.g., flow, feed, power, etc.) and output (e.g., temperature, pressure, etc.) characteristics associated with it. In recent years, model predictive control ("MPC") techniques have been used to optimize certain processes as a function of such characteristics. One MPC technique uses algorithmic representations of certain processes to estimate characteristic values (represented as parameters, variables, etc.) associated with them that can be used to better control such processes. In recent years, physical, economic and other factors have been incorporated into control systems for these associated processes. Examples of such techniques are described in U.S. Pat. No. 5,351,184 entitled "METHOD OF MULTIVARIABLE PREDICTIVE CONTROL UTILIZING RANGE CONTROL;" U.S. Pat. No. 5,561,599 entitled "METHOD OF INCORPORATING INDEPENDENT FEEDFORWARD CONTROL IN A MULTIVARIABLE PREDICTIVE CONTROLLER;" U.S. Pat. No. 5,572,420 entitled "METHOD OF OPTIMAL CONTROLLER DESIGN OF MULTIVARIABLE PREDICTIVE CONTROL UTILIZING RANGE CONTROL;" and U.S. Pat. No. 5,574,638 entitled "METHOD OF OPTIMAL SCALING OF VARIABLES IN A MULTIVARIABLE PREDICTIVE CONTROLLER UTILIZING RANGE CONTROL," all of which are commonly owned along by the assignee of the present invention and incorporated herein by reference for all purposes (the foregoing issues patents and U.S. patent application Ser. No. 08/490,499, now U.S. Pat. NO. 5,758,047, previously incorporated herein by reference, are collectively referred to hereinafter as the "HONEYWELL Patents and Application").
A problem however is that such optimization efforts, when applied to specific processes, are non-cooperative (non-global or non-facility wide) and may, and all too often do, detrimentally impact the efficiency of the process facility as a whole. One approach to resolve this global problem has been to: (1) communicate all pertinent process information from the local controllers to a centralized controller, and (2) establish a "selective" master-slave relationship between the controllers such that: (a) the local controllers continue to locally optimize their respective associated process, and (b) the centralized controller is capable of responding to certain of the pertinent process information to direct particular process(es) to operate at a certain level (e.g., stop, reduced utilization, etc.).
A primary problem with this approach is that it is responsive to emergency situations or extreme circumstances, and as such fails to provide a cooperative (global or facility wide) approach that optimizes not only individual processes, but the facility as a whole. What is needed in the art is a powerful and flexible means for dynamically optimizing a process facility as a whole through a cooperation between a global facility control and the many local (individual or group) process controllers.